Learning from failure: Trend analysis for compromised accounts and passwords

计算机账户被攻破是有原因的：一些人使用弱密码；一些人造成的数字消耗比正常情况下高得多；一些人处于高风险职位(例如，高管、会计)，并暴露在太多的攻击之下。从被攻破的账户和密码使用中吸取教训，将有助于人们设计更好的防御技术。遵循这一原则，该Engage项目将开发一套新的算法，自动学习受攻击账户和密码使用的趋势，并识别更危险的潜在威胁。该项目计划回答以下与计算机安全有关的问题：如何以及在哪里从受攻击账户的公开可用数据中检索相关信息？大多数密码有多不安全？暴力破解/猜猜他们需要多长时间？所使用的密码是否遵循任何最佳实践(例如，字母+数字)？是否与密码安全和用户的专业职位(例如，高管、经理和会计)相关？哪些客户最有可能受到攻击，他们所在的行业是什么？被攻破的账户是否存在比正常情况下更高的数字耗尽？有了从趋势分析中学到的模式，我们能否建立一个模型来预测哪些账户可能受到攻击？该模型还能就如何修补可能被入侵的账户的安全问题提出建议吗？该项目的输出包括：(1)一个数据收集模块，可以从来自黑暗网络的被泄露账户的公开可用数据中检索并存储信息；(2)一套算法，可以在趋势分析中回答上述提出的问题；(3)一个预测模型，可以预测用户被攻击的可能性。

Computer accounts get compromised for reasons: some used weak passwords; some created a digital exhaustmuch higher than normal; some were at high-risk positions (e.g., executives, accounting) and were exposed totoo many attacks. Learning lessons from compromised accounts and password use would help people designbetter defense technologies. Following this principle, this Engage project is to develop a set of new algorithms,which automatically learn the trends in compromised accounts and password use and identify potential threatsearlier. The project plans to answer the following questions related to computer security: how and where toretrieve relevant information from publicly available data on compromised accounts? How insecure are mostpasswords? How long would it take to brute force crack/guess them? Do the passwords being used follow anybest practice (e.g., letters + numbers)? Are there correlations with password security and the user's professionalpositions (e.g., executives, managers, and accounting)? Who are the accounts most likely to get compromisedand what industry are they in? Do the compromised accounts have a higher than normal digital exhaustpresence? With the patterns learnt from trend analysis, could we build a model to predict which accounts arelikely to get compromised? Could the model also give recommendations on how to patch the security problemsof accounts that are likely to get compromised?The output of this project includes: (1) a data collection module that can retrieve and store information frompublicly available data on compromised accounts from dark web, (2) a set of algorithms that can answer theabove-raised questions in trend analysis, and (3) a prediction model that can predict a user's likelihood to becompromised.